Anomaly Detection in Autonomous Driving: A Survey
April 17, 2022 Β· Declared Dead Β· π 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Daniel Bogdoll, Maximilian Nitsche, J. Marius ZΓΆllner
arXiv ID
2204.07974
Category
cs.RO: Robotics
Citations
170
Venue
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Last Checked
2 months ago
Abstract
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Robotics
π
π
Old Age
R.I.P.
π»
Ghosted
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
R.I.P.
π»
Ghosted
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
R.I.P.
π»
Ghosted
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
R.I.P.
π»
Ghosted
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
R.I.P.
π»
Ghosted
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted